Implications of the vocabulary gap in product search.. The vocabulary mismatch between query and document poses a critical challenge in search [1]}.
The vocabulary gap occurs when documents and queries, represented as a bag-of-words, use different terms to describe the same concepts. While BM25 [2]} continues to be a reliable work horse in practical search engines, there is a growing collection of neural learning to match methods aimed specifically at overcoming the vocabulary gap. These methods go beyond lexical matching by representing queries and documents in finite-dimensional vector spaces and learning their degree of similarity in this space [3]}, [4]}. In product search, the vocabulary gap may be a larger problem than in other IR domains [5]}. Product titles and queries tend to be short, and titles are not necessarily well-structured sentences but consist of phrases or simple combinations of keywords.
